Biology subjects

Kil, D.

Publications and source records attributed to Kil, D..

3 recordsLinked to original sources

bioRxiv2025

Continuous monitoring of cerebrovascular autoregulation using functional ultrasound imaging in the piglet brain.

Continuous real-time assessment of cerebral blood flow (CBF) and cerebrovascular autoregulation (CA) remains a major unmet clinical need in acute brain injury. Methods such as laser Doppler flowmetry (LDF), transcranial Doppler, or indirect indices lack accuracy and robustness. Functional ultrasound (fUS) is an emerging modality combining high spatiotemporal resolution, large field-of-view, and sensitivity to blood velocity and volume, making it a promising neuromonitoring tool. Piglets were equipped with arterial blood pressure (ABP), intracranial pressure (ICP), and LDF probes, plus cranial windows for fUS and red blood cell (RBC) flux imaging. CA was challenged by non-pharmacological ABP manipulation via intraaortic or intracaval balloon inflation. fUS hemodynamic parameters were compared with other modaliters across a CPP range of 10-150 mmHg. fUS provided continuous, stable intensity- and velocity-derived parameters across vessels types. CBF estimates correlated strongly with RBC flux and showed reproducibility comparable to LDF, with lower inter-animal variability. Autoregulation breakpoints were reliably identified by fUS, particularly the lower limit, while the upper limit was more variable. Parcellation confirmed robustness of fUS across brain regions. fUS images CBF and CA with higher stability and reproducibility than standard approaches, supporting its applicability for bedside neuromonitoring and clinical translation.

neuroscience↗

bioRxiv2024

Functional ultrasound imaging and neuronal activity: how accurate is the spatiotemporal match?

Over the last decade, functional ultrasound (fUS) has risen as a critical tool in functional neuroimaging, leveraging hemodynamic changes to infer neural activity indirectly. Recent studies have established a strong correlation between neural spike rates (SR) and functional ultrasound signals. However, understanding their spatial distribution and variability across different brain areas is required to thoroughly interpret fUS signals. In this regard, we conducted simultaneous fUS imaging and Neuropixels recordings during stimulus-evoked activity in awake mice within three regions the visual pathway. Our findings indicate that the temporal dynamics of fUS and SR signals are linearly correlated, though the correlation coefficients vary among visual regions. Conversely, the spatial correlation between the two signals remains consistent across all regions with a spread of approximately 300 micrometers. Finally, we introduce a model that integrates the spatial and temporal components of the fUS signal, allowing for a more accurate interpretation of fUS images.

neuroscience↗

bioRxiv2022

A deep learning classification task for accurate brain navigation during functional ultrasound imaging

Positioning and navigation are essential components of neuroimaging as they improve the quality and reliability of data acquisition, leading to advances in diagnosis, treatment outcomes, and fundamental understanding of the brain. Functional ultrasound (fUS) imaging is an emerging technology providing high-resolution images of the brain vasculature, allowing for the monitoring of brain activity. However, as the technology is relatively new, there is no standardized tool for inferring the position in the brain from the vascular images. This study presents a deep learning-based framework designed to address this challenge. Our approach uses an image classification task coupled with a regression on the resulting probabilities to determine the position of a single image. We conducted experiments using a dataset of 51 rat brain scans to evaluate its performance. The training positions were extracted at intervals of 375 {micro}m, resulting in a positioning error of 176 {micro}m. Further GradCAM analysis revealed that the predictions were primarily driven by subcortical vascular structures. Finally, we assessed the robustness of our method in a cortical stroke where the brain vasculature is severely impaired. Remarkably, no specific increase in the number of misclassifications was observed, confirming the methods reliability in challenging conditions. Overall, our framework provides accurate and flexible positioning, not relying on a pre-registered reference but on conserved vascular patterns.

neuroscience↗